Results 81 to 90 of about 16,710 (257)
A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws
Advanced Intelligent Discovery, EarlyView.The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows +7 more
wiley +1 more source
Adaptation of Symmetric Positive Semi-Definite Matrices for the Analysis of Textured Images
Cybernetics and Information Technologies, 2018 This paper addresses the analysis of textured images using the symmetric positive semi-definite matrix. In particular, a field of symmetric positive semi-definite matrices is used to estimate the structural information represented by the local ...
Akl Adib
doaj +1 more source
Information Submanifold Based on SPD Matrices and Its Applications to Sensor Networks
Entropy, 2017 In this paper, firstly, manifoldPD(n)consisting of alln×nsymmetric positive-definite matrices is introduced based on matrix information geometry; Secondly, the geometrical structures of information submanifold ofPD(n)are presented including metric ...
Hao Xu, Huafei Sun, Aung Naing Win
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Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
Advanced Intelligent Discovery, EarlyView.This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences [PDF]
, 2016 This paper introduces sparse coding and dictionary learning for symmetric positive definite (SPD) matrices, which are often used in machine learning, computer vision, and related areas. Unlike traditional sparse coding schemes that work in vector spaces,
Lovell, Brian +3 more
core +1 more source
On the Iterative Method for the System of Nonlinear Matrix Equations
Abstract and Applied Analysis, 2013 The positive definite solutions for the system of nonlinear matrix equations are considered, where are two positive integers and A, B are nonsingular complex matrices.
Asmaa M. Al-Dubiban
doaj +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai +3 more
wiley +1 more source
A generalization of the graph Laplacian with application to a distributed consensus algorithm
International Journal of Applied Mathematics and Computer Science, 2015 In order to describe the interconnection among agents with multi-dimensional states, we generalize the notion of a graph Laplacian by extending the adjacency weights (or weighted interconnection coefficients) from scalars to matrices.
Zhai Guisheng
doaj +1 more source
Advanced Intelligent Systems, EarlyView.A hybrid mobile robot with a modular Variable‐Stiffness Bridge transitions between a rigid locomotion platform and a flexible, shape‐conforming body. By enclosing objects within its deformable structure rather than relying on dedicated end effectors, the robot achieves orientation‐regulated planar transport, with conformal contact quality shown to ...
Luiza Labazanova +5 more
wiley +1 more source
A trace inequality for positive definite matrices [PDF]
, 2009 International audienceIn this note we prove that Tr (MN+ PQ)>= 0 when the following two conditions are met: (i) the matrices M, N, P, Q are structured as follows: M = A -B, N = inv(B)-inv(A), P = C-D, Q =inv (B+D)-inv(A+C), where inv(X) denotes the ...
Elena-veronica Belmega +5 more
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